scholarly journals Authenticated Medical Image Transmission using Enhanced Reversible Data Hiding (RDH) with NNP2 Algorithm and Rhombus Prediction

2019 ◽  
Vol 8 (4) ◽  
pp. 12188-12192
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Medical Information ◽  
Medical Images ◽  
Chinese Remainder Theorem ◽  
Image Transmission ◽  
Hiding Capacity ◽  
Distortion Level ◽  
Home Monitor ◽  
Nearest Neighborhood

During the last few years, the medical information of concerned patient is transferred from one doctor to another doctor via internet for better diagnosis and studies. Transferring medical information over a transmission medium is known as telemedicine. Telemedicine has been used to overcome distance barriers and to improve access to medical service. The telemedicine application includes emergency treatment, home monitor, military applications, and medical education. These medical images are corrupted by hackers when it is transferred through internet. Hence security of medical images is necessary. Watermarking is used for providing security while transferring medical images. Reversible Data Hiding (RDH) is one of the efficient methods for secure transmission of medical images. In this method, data hiding capacity is very small and the distortion level of recovers images is very large. To avoid these drawbacks, Nearest Neighborhood Pixel Prediction (NNP2 ) algorithm based on Chinese Remainder Theorem (CRT) is proposed and Rhombus Prediction is applied in NNP2 to increase data hiding capacity. The distortion level is reduced by Histogram Shifting. The performance of proposed method is evaluated using PSNR for number of medical images. The results shows that the proposed method gives good results when compared with traditional methods.

scholarly journals Two-Bit Embedding Histogram-Prediction-Error Based Reversible Data Hiding for Medical Images with Smooth Area

Computers ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 152
Author(s):  
Ching-Yu Yang ◽  
Ja-Ling Wu
Keyword(s):  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
Medical Images ◽  
High Capacity ◽  
Personal Privacy ◽  
Hiding Capacity ◽  
X Ray ◽  
Magnetic Resonance Imaging Mri ◽  
Hidden Data

During medical treatment, personal privacy is involved and must be protected. Healthcare institutions have to keep medical images or health information secret unless they have permission from the data owner to disclose them. Reversible data hiding (RDH) is a technique that embeds metadata into an image and can be recovered without any distortion after the hidden data have been extracted. This work aims to develop a fully reversible two-bit embedding RDH algorithm with a large hiding capacity for medical images. Medical images can be partitioned into regions of interest (ROI) and regions of noninterest (RONI). ROI is informative with semantic meanings essential for clinical applications and diagnosis and cannot tolerate subtle changes. Therefore, we utilize histogram shifting and prediction error to embed metadata into RONI. In addition, our embedding algorithm minimizes the side effect to ROI as much as possible. To verify the effectiveness of the proposed approach, we benchmarked three types of medical images in DICOM format, namely X-ray photography (X-ray), computer tomography (CT), and magnetic resonance imaging (MRI). Experimental results show that most of the hidden data have been embedded in RONI, and the performance achieves high capacity and leaves less visible distortion to ROIs.

Differential Image Compression for Telemedicine: A Novel Approach

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Adnan Alam Khan ◽  
Dr. Asadullah Shah ◽  
Saghir Muhammad
Keyword(s):  
Image Compression ◽  
Medical Information ◽  
Emerging Technologies ◽  
Medical Images ◽  
Image Transmission ◽  
Medical Doctors ◽  
Medical Sciences ◽  
Compression Method ◽  
Paramedical Staff ◽  
Novel Approach

Telemedicine is one of the most emerging technologies of applied medical sciences. Medical information related to patients is transmitted and stored for references and consultations. Medical images occupy huge space; in order to transmit these images may delay the process of image transmission in critical times. Image compression techniques provide a better solution to combat bandwidth scarcity problems, and transmit same image in a much lower bandwidth requirements, more faster and at the same time maintain quality. In this paper a differential image compression method is developed in which medical images are taken from a wounded patient and are compressed to reduce the bit rate of these images. Results indicate that on average 25% compression on images is achieved with 3.5 MOS taken from medical doctors and other paramedical staff the ultimately user of the images.

scholarly journals Blockchain-Based Reversible Data Hiding for Securing Medical Images

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Ji-Hwei Horng ◽  
Ching-Chun Chang ◽  
Guan-Long Li ◽  
Wai-Kong Lee ◽  
Seong Oun Hwang
Keyword(s):  
Data Hiding ◽  
Private Information ◽  
Medical Diagnosis ◽  
Reversible Data Hiding ◽  
Secure Communication ◽  
Medical Images ◽  
Personal Information ◽  
Embedding Capacity ◽  
Image Block ◽  
Encrypted Images

Medical images carry a lot of important information for making a medical diagnosis. Since the medical images need to be communicated frequently to allow timely and accurate diagnosis, it has become a target for malicious attacks. Hence, medical images are protected through encryption algorithms. Recently, reversible data hiding on the encrypted images (RDHEI) schemes are employed to embed private information into the medical images. This allows effective and secure communication, wherein the privately embedded information (e.g., medical records and personal information) is very useful to the medical diagnosis. However, existing RDHEI schemes still suffer from low embedding capacity, which limits their applicability. Besides, such solution still lacks a good mechanism to ensure its integrity and traceability. To resolve these issues, a novel approach based on image block-wise encryption and histogram shifting is proposed to provide more embedding capacity in the encrypted images. The embedding rate is over 0.8 bpp for typical medical images. On top of that, a blockchain-based system for RDHEI is proposed to resolve the traceability. The private information is stored on the blockchain together with the hash value of the original medical image. This allows traceability of all the medical images communicated over the proposed blockchain network.

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